Which ONE of the following options is an example that BEST describes a system with Al- based autonomous functions?
SELECT ONE OPTION

1. A. A system that utilizes human beings for all important decisions.
2. B. A fully automated manufacturing plant that uses no software.
3. C. A system that utilizes a tool like Selenium.
4. D. A system that is fully able to respond to its environment.

Correct Answer: D
AI-Based Autonomous Functions: An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.
Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems.

Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION

1. A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
2. B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
3. C. These attacks can't be prevented by retraining the model with these examplesaugmented to the training data.
4. D. These examples are model specific and are not likely to cause another model trained on same task to fail.

Correct Answer: D
✑ A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
✑ B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
✑ C. These attacks can't be prevented by retraining the model with these examples augmented to the training data.
✑ D. These examples are model specific and are not likely to cause another model trained on the same task to fail.
Therefore, the correct answer is D because adversarial examples are typically model- specific and may not cause another model trained on the same task to fail.

Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION

1. A. Challenges resulting from low accuracy of the models.
2. B. The challenge of mimicking undefined scenarios generated due to self-learning
3. C. The challenge of providing explainability to the decisions made by the system.
4. D. Challenges in the creation of scenarios of human handover for autonomous systems.

Correct Answer: D
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let??s evaluate each option:
✑ A. Challenges resulting from low accuracy of the models.
✑ B. The challenge of mimicking undefined scenarios generated due to self-learning.
✑ C. The challenge of providing explainability to the decisions made by the system.
✑ D. Challenges in the creation of scenarios of human handover for autonomous systems.
Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.

Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION

1. A. Identifying the relationship between developers and the modules developed by them.
2. B. Search of similar code based on natural language processing.
3. C. Clustering of similar code modules to predict based on similarity.
4. D. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.

Correct Answer: D
Defect prediction models aim to identify parts of the software that are likely to contain defects by analyzing historical data and code quality metrics. The primary goal is to use this predictive information to allocate testing and maintenance resources effectively. Let's break down why option D is the correct choice:
✑ Understanding Classification Models:
✑ Input Data - Code Quality Metrics:
✑ Historical Data:
✑ Why Option D is Correct:
✑ Eliminating Other Options:
References:
✑ ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques including classification models for defect prediction.
✑ "Using AI for Defect Prediction" (ISTQB CT-AI Syllabus, Section 11.5.1).

Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION

1. A. The data pipeline
2. B. The quality of the labeling
3. C. Biased data
4. D. The number of classes

Correct Answer: D
Factors Affecting ML Functional Performance: The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.